2024年11月26日 · Using vLLM on AWS Trainium and Inferentia makes it possible to host LLMs for high performance inference and scalability. In this post, we will walk you through how you can quickly deploy Meta’s latest Llama models, using vLLM on an Amazon Elastic Compute Cloud (Amazon EC2) Inf2 instance.

2023年11月22日 · This tutorial will walk you through steps on how to host LLM model using AWS EC2 instance, vLLM, Langchain, serve LLM inference using FastAPI, use LLM caching mechanism to cache LLM requests for...

2024年2月13日 · In this article we will show how to deploy some of the best LLMs on AWS EC2: LLaMA 3 70B, Mistral 7B, and Mixtral 8x7B. We will use an advanced inference engine that supports batch inference in order to maximise the throughput: vLLM.

2024年12月9日 · VLM效果好主要是由LLM和vision backbone这俩单模态模型效果好推动的 完全自回归的模型架构，优于cross-attention架构 projector模块作用很大（降token），可以实现提高模型推理效率、不损害模型性能

[2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post here. [2025/01] We hosted the eighth vLLM meetup with Google Cloud!

2024年10月29日 · We're building a chatbot and aiming for consistent, responsive performance under concurrent user loads. At ~15 requests, processing delays reach up to 30 seconds before streaming begins. Though streaming speed is good, we'd prefer requests to start sooner, even if they stream slower. We're also seeking optimal vLLM settings for our hardware.

2025年1月21日 · 自从谷歌提出ViT、Open AI发布CLIP，视觉语言模型（VLM）便成为了研究热点，凭借跨模态处理和理解能力，以及零样本学习方法，为CV领域带来了重大革新，今年CVPR'24自动驾驶挑战赛中，VLM也是参赛人数最多的赛道，围绕环境感知提升等，应用方案百花齐放，而 ...

2025年2月14日 · 本文将详细介绍如何配置 DeepSeek-VL2 的运行环境，并展示如何下载、运行模型以及使用多 GPU 支持。 本文内容适用于需要快速上手 DeepSeek-VL2 的开发者。 什么是 VLM？ VLM 是 Vision-Language Model （视觉-语言模型）的缩写。 它是一种结合了计算机视觉和自然语言处理技术的 多模态 模型。 VLM 能够同时理解和生成图像与文本信息，适用于多种跨模态任务，例如： VLM 的核心在于将视觉特征（来自图像）和语言特征（来自文本）进行联合建 …

2024年1月12日 · In this article we focus on deploying a small large language model, Tiny-Llama, on an AWS instance called EC2. List of tools I’ve used for this project: Nginx: is an HTTP and reverse proxy server. I use it to connect the FastAPI server to AWS. HuggingFace: is a platform to host and collaborate on unlimited models, datasets, and applications.

You can use the LMI to easily run vLLM on Amazon SageMaker. However, the version of vLLM supported by LMI lags several versions behind the latest community version. If you want to run the latest version, try this repo! Make sure you have the following tools installed: 1. Set Environment Variables. Start by setting up some environment variables.